Currently, most of the smart phones, tablets and other web platforms are equipped with a remote application which allows users to control the video viewing experience remotely. Generally, setting up the remote application is simple and they are configured to work with multiple Customer Premise Equipment (CPE) devices including Set-Top box (STB), Digital television (DTV) and Digital Video Recorder (DVR) within a home network. The usage of the remote applications has been increased with increase in the use of smartphones, web platform and other second screen platforms within the home network. Further, the remote applications provide better user experience as they have the ability to bring in new functionality in the remote control without the need for any hardware upgrades. Also, the universal remote application has the ability to add multiple remotes for different CPE devices such as DTV, DVR, STB, and Audio Systems with a single remote application being switched seamlessly between these devices to avoid the hassle of operating multiple remotes simultaneously.
At present, most of these remote applications are used for controlling the CPE devices. However, for such remote applications, identification and also knowledge of the actual “USER” using the remote application is minimal because CPE remote applications are not based on user profile. Further, it is not possible to track users' video consumption pattern at the remote application though these applications run on personal devices such as mobile phones. The other problems with the existing remote applications are that though there is only a single active remote control application controlling the CPE at any given point in time and is available with one viewer, other viewers within the room do not get to access other activities include, but are not limited to, schedule viewing, recording through their remote applications. This is because the CPE does not recognize multiple remote applications within the home concurrently.
Currently, a lot of effort and money is spent on performing analytics in the backend to interpret video consumption patterns of the user, since different users access the CPE devices using the same remote and specific user information is not available when the remote is being used by the user. Further, the analytics is currently performed on video consumption patterns without the actual knowledge of the specific user on home devices such as STB and mobile platforms. Hence, there is a possibility of high number of false positives in terms of assessing the user behavior. This results in generic recommendations rather than targeted recommendations and also leads in incorrect advertisement schemes rolled out for a user who has not actually consumed the specific content on which the advertisement was based.
One of the conventional methods discloses a technique for mobile content tracking. This technique offers a mechanism for channel surfing and program viewing using a mobile device. The technique also enables outsourcing the functions of a remote control or set-top box to a mobile device. As a result of which the mobile device is able to track video consumption pattern at the device level, but is unable to differentiate the video consumption patterns at the next level of granularity, which is user level.
The issues mainly faced in the existing systems are that, they do not account for multiple remote applications controlling the video viewing experience wherein the control is in active or passive mode and monitoring the video consumption pattern for every user thereby providing personalized video viewing suggestions.